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» Multiple target detection using Bayesian learning
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NN
1997
Springer
174views Neural Networks» more  NN 1997»
14 years 2 months ago
Learning Dynamic Bayesian Networks
Bayesian networks are directed acyclic graphs that represent dependencies between variables in a probabilistic model. Many time series models, including the hidden Markov models (H...
Zoubin Ghahramani
RECOMB
2000
Springer
14 years 1 months ago
Using Bayesian networks to analyze expression data
DNA hybridization arrays simultaneously measure the expression level for thousands of genes. These measurements provide a "snapshot" of transcription levels within the c...
Nir Friedman, Michal Linial, Iftach Nachman, Dana ...
ICTAI
2008
IEEE
14 years 4 months ago
Using Imputation Techniques to Help Learn Accurate Classifiers
It is difficult to learn good classifiers when training data is missing attribute values. Conventional techniques for dealing with such omissions, such as mean imputation, general...
Xiaoyuan Su, Taghi M. Khoshgoftaar, Russell Greine...
SAC
2010
ACM
14 years 4 months ago
Coverage-hole trap model in target tracking using distributed relay-robot network
Target tracking is an important issue in wireless sensor network applications. In this paper, we design a Coverage-Hole Trap Model (CTM) based on a system that contains one moving...
Huawei Miao, Chia-Ching Ooi, Xiaowen Wu, Christian...
SMC
2007
IEEE
118views Control Systems» more  SMC 2007»
14 years 4 months ago
One-class learning with multi-objective genetic programming
One-class classification naturally only provides one class of exemplars on which to construct the classification model. In this work, multiobjective genetic programming (GP) all...
Robert Curry, Malcolm I. Heywood